ARCHIVES

Research Article

Automating Pacman with Deep Q-Learning Using PYgame

G.VIJAY KUMAR1 N. SAI KUMAR2 U.VARSHA3 SK.AFROZ4
1Assistant Professor, Computer Science and Engineering, CMR Technical Campus, Hyderabad, India. 234Student, Computer Science and Engineering, CMR Technical Campus, Hyderabad, India.

Published Online: May-June 2022

Pages: 437-439

Cite this article

No DOI

Abstract

Abstract: A computer program capable of doing a human-level performance on a number of games. Just like a human, the algorithm played based on its vision of the screen. Starting from scratch, it discovered gameplay strategies that let it meet (and in many cases, exceed) human benchmarks. In the years since researchers have made a number of improvements that super-charge performance and solve games faster than ever before. We've been working to implement these advancements in Keras — the open-source, highly accessible machine learning framework— and in this post, we'll walk through the details of how they work and how they can be used to master Ms. Pac-man.

Related Articles

2022

A Review on Bamboo Reinforced Concrete Beam

2022

FARMERS AGRICULTURAL PORTAL

2022

Sentiment Analysis of Religious Tweets

2022

Enhancement of beam strength by using bamboo as reinforcement in place of steel bars

2022

A Review on Anomaly Detection using PYOD Package

2022

Traffic Rule Violation Detection system

Share Article

X
LinkedIn
Facebook
WhatsApp

Or copy link

https://theijire.com/archives/automating-pacman-with-deep-q-learning-using-pygame

*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.